The effect of spatiotemporal displacement on the integration of shape information

Green, Robert J.; Dickinson, J. Edwin; Badcock, David R.

doi:10.1167/18.5.4

Cited by 8 publications

(10 citation statements)

References 56 publications

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“…Global shape processing, in our case based on the integration of local information around the shape's contour, can aid in the detection and discrimination of different shapes. This has been examined by numerous previous studies using radial frequency (RF) patterns (Almeida, Dickinson, Maybery, Badcock, & Badcock, 2013;Almeida, Dickinson, Maybery, Badcock, & Badcock, 2014;Baldwin et al, 2016;Bell, Badcock, Wilson, & Wilkinson, 2007;Bell, Dickinson, & Badcock, 2008;Dickinson, Han, Bell, & Badcock, 2010;Green, Dickinson, & Badcock, 2018a, 2018bLoffler, Wilson, & Wilkinson, 2003;Schmidtmann, Kennedy, Orbach, & Loffler, 2012;Wilkinson, Wilson, & Habak, 1998;Wilson & Propp, 2015). RF patterns are derived from circles by sinusoidally modulating their radius as a function of the polar angle.…”

Section: Introductionmentioning

confidence: 99%

Convergent evidence for global processing of shape

2018

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There is an ongoing debate over whether there is convincing evidence in support of global contour integration in shape discrimination tasks, particularly when using radial frequency (RF) patterns as stimuli (Baldwin, Schmidtmann, Kingdom, & Hess, 2016). The objection lies in the previous use of high-threshold theory (HTT), rather than signal detection theory (SDT) to model the probability summation estimates of observer thresholds to determine whether integration of information is occurring around the contour. Here we used a discrimination at threshold method to establish evidence of global processing of two frequently used RF patterns (RF3 and RF5) that does not require mathematical modeling. To provide a bridge between current and past research we examined the two proposed methods, finding that HTT produced probability summation estimates that were more conservative than SDT (when an appropriate number of channels was used to generate estimates). We found no difference in observer thresholds when an RF pattern was presented as the only test stimulus in a block of trials or when two RF patterns were interleaved, and no evidence for a decrease in psychometric slopes with increasing numbers of stimulus elements. These findings are contrary to what is predicted by SDT for a stimulus whose detection conforms to probability summation. Therefore, our results find no evidence that supports probability summation, further demonstrating the importance of using random phase RF patterns while measuring integration around a contour and providing strong evidence for global shape processing around low frequency RF patterns.

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Section: Introductionmentioning

confidence: 99%

Convergent evidence for global processing of shape

2018

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“…In a series of studies that examined the effect of spatial uncertainty on shape discrimination thresholds, Green and colleagues (2017Green and colleagues ( , 2018aGreen and colleagues ( , 2018bGreen and colleagues ( , 2018c found that sensitivity to curvature differences between two shapes was greatest when the phase (i.e., angular position) of the shapes varied from trial to trial. This finding suggests that spatial uncertainty affects sensitivity to contour curvature (Green et al, 2017(Green et al, , 2018a(Green et al, , 2018b(Green et al, , 2018c), but does not reveal how spatial uncertainty affects sensitivity to curvature. Baldwin et al (2016) examined the effect of spatial uncertainty on curvature detection thresholds as a function of the number of modulated cycles appearing along the contour of a shape.…”

Section: Introductionmentioning

confidence: 84%

Sensitivity to curvature deformations along closed contours

2019

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To test the assumption that detection thresholds for curvature deformations are invariant across polar angles, we used a novel stimulus class we call Difference of Gaussian (DoG) contours that allowed us to independently manipulate the amplitude, angular frequency, and polar angle of curvature of a closed-contour shape while measuring contourcurvature thresholds. Our results demonstrate that (a) detection thresholds were higher when observers were uncertain about the location of the curvature deformation, but on average, thresholds did not vary significantly across 24 polar angles; (b) the direction and magnitude of the oblique effect varies across individuals; (c) there is a strong association between detecting a contour deformation and identifying its location; (d) curvature detectors may serve as labeled lines.

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“…More importantly, the method used here generates a combination of many RF patterns, whereas most previous studies used either single component RF patterns (e.g. Bell & Badcock, 2008Bell et al, 2007;Green, Dickinson, & Badcock, 2017, 2018a, Green, Dickinson, & Badcock, 2018bHess, Achtman, & Wang, 2001;Jeffrey et al, 2002;Schmidtmann et al, 2012;Loffler et al, 2003), combinations of two Bell et al, 2007;Lawrence et al, 2016) or three RF components (Schmidtmann, Jennings, & Kingdom, 2015). Given that the aim of this study was to explore the limits of what can be represented by compound RF patterns, the stimuli used here are combinations of an arbitrary number of RF patterns (see Methods).…”

Section: Discussionmentioning

confidence: 99%

Radial frequency patterns describe a small and perceptually distinct subset of all possible planar shapes

Schmidtmann

Fruend

2019

Vision Research

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Radial frequency patterns describe a small and perceptually distinct subset of all possible planar shapes.Schmidtmann, GThe visual system is exposed to a vast number of shapes and objects. Yet, human object recognition is effortless, fast and largely independent of naturally occurring transformations such as position and scale. The precise mechanisms of shape encoding are still largely unknown. Radial frequency (RF) patterns are a special class of closed contours defined by modulation of a circle's radius. These patterns have been frequently and successfully used as stimuli in vision science to investigate aspects of shape processing. Given their mathematical properties, RF patterns can not represent any arbitrary shape, but the ability to generate more complex, biologically relevant, shapes depicting the outlines of objects such as fruits or human heads raises the possibility that RF patterns span a representative subset of possible shapes. However, this assumption has not been tested before. Here we show that only a small fraction of all possible shapes can be represented by RF patterns and that this small fraction is perceptually distinct from the general class of all possible shapes. Specifically, we derive a general measure for the distance of a given shape's outline from the set of RF patterns, allowing us to scan large numbers of object outlines automatically. We find that only between 1% and 6% of naturally smooth outlines can be exactly represented by RF patterns. We present results from a visual search experiment, which revealed that searching an RF pattern among non-radial frequency patterns is efficient, whereas searching an RF pattern among other RF patterns is inefficient (and vice versa). These results suggest that RF patterns represent only a restricted subset of possible planar shapes and that results obtained with this special class of stimuli can not simply be expected to generalise to any arbitrary planar shape.

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The effect of spatiotemporal displacement on the integration of shape information

Cited by 8 publications

References 56 publications

Convergent evidence for global processing of shape

Convergent evidence for global processing of shape

Sensitivity to curvature deformations along closed contours

Radial frequency patterns describe a small and perceptually distinct subset of all possible planar shapes

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